Abstract
Open banking enables individual customers to own their banking data, which provides fundamental support for the boosting of a new ecosystem of data marketplaces and financial services. In the near future, it is foreseeable to have decentralized data ownership in the finance sector using federated learning. This is a just-in-time technology that can learn intelligent models in a decentralized training manner. The most attractive aspect of federated learning is its ability to decompose model training into a centralized server and distributed nodes without collecting private data. This kind of decomposed learning framework has great potential to protect users’ privacy and sensitive data. Therefore, federated learning combines naturally with an open banking data marketplaces. This chapter will discuss the possible challenges for applying federated learning in the context of open banking, and the corresponding solutions have been explored as well.
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References
Abadi, M., et al.: Deep learning with differential privacy. In: The 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 308–318 (2016)
Arivazhagan, M.G., Aggarwal, V., Singh, A.K., Choudhary, S.: Federated learning with personalization layers. arXiv:1912.00818 (2019)
Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Wortman, J.: Learning bounds for domain adaptation. In: Advances in Neural Information Processing Systems, pp. 129–136 (2008)
Bonawitz, K., et al.: Practical secure aggregation for privacy-preserving machine learning. In: The 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1175–1191 (2017)
Briggs, C., Fan, Z., Andras, P.: Federated learning with hierarchical clustering of local updates to improve training on Non-IID data. arXiv:2004.11791 (2020)
Brodsky, L., Oakes, L.: Data Sharing and Open Banking. McKinsey Company, New York (2017)
Chesbrough, H., Vanhaverbeke, W., West, J.: New Frontiers in Open Innovation. OUP Oxford, Oxford, (2014)
Chesbrough, H.W.: Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business Press, Brighton (2003)
Deng, Y., Kamani, M.M., Mahdavi, M.: Adaptive personalized federated learning. arXiv:2003.13461 (2020)
Dinh, C.T., Tran, N.H., Nguyen, T.D.: Personalized federated learning with Moreau envelopes. arXiv:2006.08848 (2020)
Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79228-4_1
European Parliament, Council of the European Union: General data protection regulation (GDPR). Technical report. The European Parliament and The Council of The European Union (2016). https://eur-lex.europa.eu/eli/reg/2016/679/oj
Ghosh, A., Chung, J., Yin, D., Ramchandran, K.: An efficient framework for clustered federated learning. arXiv:2006.04088 (2020)
Open Banking Working Group and others: The open banking standard. Technical report, working paper, Open Data Institute (2018)
Guha, N., Talwalkar, A., Smith, V.: One-shot federated learning. arXiv:1902.11175 (2019)
Hanzely, F., Richtárik, P.: Federated learning of a mixture of global and local models. arXiv:2002.05516 (2020)
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv:1503.02531 (2015)
Jeong, E., Oh, S., Kim, H., Park, J., Bennis, M., Kim, S.L.: Communication-efficient on-device machine learning: federated distillation and augmentation under Non-IID private data. arXiv:1811.11479 (2018)
Jiang, J., Ji, S., Long, G.: Decentralized knowledge acquisition for mobile internet applications. World Wide Web, pp. 1–17 (2020)
Kang, J., Xiong, Z., Niyato, D., Xie, S., Zhang, J.: Incentive mechanism for reliable federated learning: a joint optimization approach to combining reputation and contract theory. IEEE Internet Things J. 6(6), 10700–10714 (2019)
Khan, L.U., et al.: Federated learning for edge networks: resource optimization and incentive mechanism. arXiv:1911.05642 (2019)
Li, D., Wang, J.: FedMD: heterogenous federated learning via model distillation. arXiv:1910.03581 (2019)
Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. arXiv:1812.06127 (2018)
Liang, P.P., Liu, T., Ziyin, L., Salakhutdinov, R., Morency, L.P.: Think locally, act globally: federated learning with local and global representations. arXiv:2001.01523 (2020)
Mansour, Y., Mohri, M., Ro, J., Suresh, A.T.: Three approaches for personalization with applications to federated learning. arXiv:2002.10619 (2020)
Mirshghallah, F., Taram, M., Vepakomma, P., Singh, A., Raskar, R., Esmaeilzadeh, H.: Privacy in deep learning: a survey. arXiv:2004.12254 (2020)
Mohri, M., Sivek, G., Suresh, A.T.: Agnostic federated learning. arXiv:1902.00146 (2019)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2009)
Preuveneers, D., Rimmer, V., Tsingenopoulos, I., Spooren, J., Joosen, W., Ilie-Zudor, E.: Chained anomaly detection models for federated learning: an intrusion detection case study. Appl. Sci. 8(12), 2663 (2018)
PYMNTS: Open banking targets SMB apps, payments data. Technical report, PYMNTS.com (2020). https://www.pymnts.com/news/b2b-payments/2020/open-banking-targets-smb-apps-payments-data/
Sattler, F., MĂĽller, K.R., Samek, W.: Clustered federated learning: model-agnostic distributed multi-task optimization under privacy constraints. arXiv:1910.01991 (2019)
Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: The 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1310–1321 (2015)
Wang, H., Yurochkin, M., Sun, Y., Papailiopoulos, D., Khazaeni, Y.: Federated learning with matched averaging. In: International Conference on Learning Representations (2020)
Xie, M., Long, G., Shen, T., Zhou, T., Wang, X., Jiang, J.: Multi-center federated learning. arXiv:2005.01026 (2020)
Yang, Q., Liu, Y., Chen, T., Tong, Y.: Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. (TIST) 10(2), 1–19 (2019)
Yang, Q., Liu, Y., Cheng, Y., Kang, Y., Chen, T., Yu, H.: Federated learning. Synth. Lect. Artif. Intell. Mach. Learn. 13(3), 1–207 (2019)
Yu, T., Bagdasaryan, E., Shmatikov, V.: Salvaging federated learning by local adaptation. arXiv:2002.04758 (2020)
Yu, Z., et al.: Federated learning based proactive content caching in edge computing. In: 2018 IEEE Global Communications Conference (GLOBECOM), pp. 1–6. IEEE (2018)
Zhan, Y., Li, P., Qu, Z., Zeng, D., Guo, S.: A learning-based incentive mechanism for federated learning. IEEE Internet Things J. (2020)
Zhang, L., Tao, X.: Open banking: what? Difficult? And how? Technical report, PwC China (2019). https://wemp.app/posts/05e3ed49-126e-408d-bf8a-ba1f86d86c88?utm_source=bottom-latest-posts
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., Chandra, V.: Federated learning with Non-IID data. arXiv:1806.00582 (2018)
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Long, G., Tan, Y., Jiang, J., Zhang, C. (2020). Federated Learning for Open Banking. In: Yang, Q., Fan, L., Yu, H. (eds) Federated Learning. Lecture Notes in Computer Science(), vol 12500. Springer, Cham. https://doi.org/10.1007/978-3-030-63076-8_17
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